Job Offers

Our Offer:

Student Assistant (W/M/D) for Transfer, Multi-task, and Representation Learning in Renewable Energy Systems at FG IES

We are currently seeking a highly motivated and qualified student assistant to join our team for an exciting project focusing on the application of transfer learning, representation learning, and multi-task learning methods for anomaly detection and predicting the remaining useful life in renewable energy systems. The project involves working with real-world datasets, and we are looking for a dedicated individual who wants to contribute to cutting-edge research and development in this field. As a student assistant, you will be involved in various tasks, including:

• Research and implementation of deep learning methods, particularly transfer and representation learning approaches for practical applications with a focus on anomaly detection and remaining useful life prediction in renewable energy systems.
• Contribution to the development of new deep learning methods.
• Testing and evaluating various solutions for specific applications.
• Analysis and interpretation of experimental results.
• Collaboration in writing scientific articles.

As a student assistant, you can expect:

• Long-term involvement in machine learning (ML) research projects.
• Access to one of the largest computing clusters in North Hesse with state-of-the-art graphics cards.
• Collaboration with a team of qualified researchers and student assistants.
• Flexibility in shaping your working hours.
• A friendly working environment.
• The opportunity to combine work with participation in seminars, projects, and thesis work.

Requirements:

• Enrollment at a university.
• Experience and knowledge in machine learning and deep learning.
• Familiarity with Python (e.g., NumPy, Scikit-learn, PyTorch, Jupyter, Matplotlib).
• Experience in software development (e.g., Git, code documentation, object-oriented programming).
• Enjoyment of teamwork.
• Good English skills.

Of Advantageous Are:

• Experience with LaTeX.
• A positive learning attitude.
• Research interest in deep learning applications.

If interested, please send your application (resume, academic transcript) with the subject "HiWi-Forschung" to ies-hiwi@uni-kassel.de.

Our Offer:

Student Assistant (W/M/D) for Reinforcement Learning in Power Grid Control at FG IES

We are currently looking for a highly motivated and qualified student assistant to join our team for an exciting project focusing on the application of Reinforcement Learning (RL) and explainability methods for power grid control. The project involves working with the grid2op library, and we are seeking an engaged individual who wants to contribute to cutting-edge research and development in this field. As a student assistant, you will be involved in various tasks, including:

• Research and implementation of Deep Learning and Reinforcement Learning methods for practical applications in power grid control.
• Contribution to the development of novel Reinforcement Learning methods tailored to specific challenges in this field.
• Experimentation with and evaluation of various solutions to enhance the efficiency and robustness of power grid control.
• Analysis and interpretation of experimental results to provide valuable insights.

Requirements:

To be successful in this role, you should:

• Be enrolled in a university program.
• Have experience and knowledge in Deep Learning and preferably in topics related to Reinforcement Learning.
• Have proficiency in Python, including libraries like NumPy, Scikit-learn, PyTorch, and experience with Jupyter Notebooks and Matplotlib.
• Have experience in software development, including version control with Git and code documentation.

Advantages:

While not mandatory, the following skills and qualities would be beneficial:

• Experience with Reinforcement Learning frameworks such as Ray RLLib, Gymnasium, and stablebaselines.
• Experience working with LaTeX for document preparation.
• A positive learning attitude and a keen interest in ongoing research in applied Reinforcement Learning and power grids.

If interested, please send your application (resume and academic transcripts) with the subject "HiWi-Forschung" to ies-hiwi@uni-kassel.de.